CN112626216A - Composition for detecting unstable state of tumor microsatellite and application thereof - Google Patents
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Abstract
The invention relates to the technical field of molecular diagnosis, in particular to a composition for detecting the instability state of a tumor microsatellite and application thereof, wherein the composition consists of PTMAP4, H3F3AP6, RPL22L1, RP11-267J23.4, MSH4, LYG1, RP11-372B4.3, TLE6, RP11-819M15.2, CIRBP-AS1, FAM151B, EIF5A, SUMO2P17, FECH and MNX1-AS 1. The invention obtains the corresponding cutoff value of each tumor by performing difference analysis by using a GSVA algorithm, and can be used for detecting the microsatellite instability (MSI) states of various tumors. Experiments prove that the method has the advantages of high accuracy and good specificity, and has good application prospect.
Description
Technical Field
The invention relates to the technical field of molecular diagnosis, in particular to a composition for detecting the unstable state of a tumor microsatellite and application thereof.
Background
Microsatellite instability (MSI), a phenomenon in which the length of the MS sequence changes due to insertion or deletion mutations during DNA replication, often caused by a functional defect in mismatch repair (MMR). The MSI phenomenon was first discovered in 1993 by Jacobs et al in colorectal cancer, is associated with carcinogenesis, and can be used for cancer detection. The MSI-H phenomenon is reported in the literature in about 15% of colorectal cancers, and differs in pathogenesis, prognosis and sensitivity to drugs compared to MSS-characterized colorectal cancers. Among solid tumors other than colorectal cancer, there are also different ratios of MSI-H phenomenon, and there are significant differences in the response rate to Keytruda among solid tumors with different MSI status. Therefore, it is very important to accurately distinguish the MSI state of a tumor patient, to improve the therapeutic effect of the patient, and to reduce the burden on the society.
At present, the MSI state detection by a multiplex fluorescence PCR-capillary electrophoresis method is an internationally recognized gold standard, the DNA of normal tissues and tumor tissue samples of the same patient is extracted, a multiplex fluorescence PCR method is adopted to amplify detection sites, amplification products are detected by capillary electrophoresis, and professional software is utilized to compare and analyze detection results of two tissue sources, so that the MSI state of the patient can be accurately typed. The method is solid and accurate, but the operation is more complicated. In addition, there are some studies reporting the use of genetic data to calculate MSI status, but all are based on DNA sequence and are complicated, and there is still a lack of MSI detection method in terms of mRNA level. Therefore, an MSI detection method with convenient operation and high accuracy is particularly important for tumor typing.
Current studies show that mismatch repair genes are closely related to MSI in hereditary nonpolyposis carcinoma of large intestine (HNPPC), and have been widely used in colon cancer. However, due to the lack of comprehensive systematic analysis of various cancer genomes, it is still possible that genes with higher sensitivity, specificity and application value can be applied to the prediction of the tumor microsatellite status. Meanwhile, the instability of the microsatellite is a continuous dynamic process, and a quantifiable index may exist. According to the invention, 1441 samples containing 6 tumors in the TCGA database and having specific MSI state information are subjected to gene expression data analysis, and 15 upregulated genes or lncRNA with the most difference are screened out: PTMAP4, H3F3AP6, RPL22L1, RP11-267J23.4, MSH4, LYG1, RP11-372B4.3, TLE6, RP11-819M15.2, CIRBP-AS1, FAM151B, EIF5A, SUMO2P17, FECH, MNX1-AS 1. Then, we set these 15 factors as a factor set, and calculate an MSI score using GSVA algorithm, thereby realizing the quantification and prediction of MSI state. The method is simple and convenient to operate, can be used for quantitative analysis, and has certain advantages in sensitivity and specificity. The composition for detecting the instability state of the tumor microsatellite and the application thereof are not reported at present.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a composition for detecting the instability of tumor microsatellites and application thereof.
In order to achieve the purpose, the invention adopts the technical scheme that:
firstly, the invention provides an application of a detection reagent in preparing a kit for evaluating the instability state of a tumor microsatellite, wherein the detection reagent consists of reagents for detecting the expression quantity of the following genes/lncRNA: PTMAP4, H3F3AP6, RPL22L1, RP11-267J23.4, MSH4, LYG1, RP11-372B4.3, TLE6, RP11-819M15.2, CIRBP-AS1, FAM151B, EIF5A, SUMO2P17, FECH, MNX1-AS 1; the detection reagent is used as a kit to realize the evaluation of the only key component of the instability state of the tumor microsatellite.
Preferably, the kit further comprises an instruction book, wherein the instruction book records the following formula:
GSVA score-2.093101 + (0.012068 × PTMAP4) - (0.008544 × H3F3AP6) + (0.062508 × RPL22L1) + (0.073353 × RP11-267J23.4) + (0.111126 × MSH4) + (0.019458 × LYG1) + (0.119968 × RP11-372B4.3) + (0.099555 × TLE6) + (0.218096 × RP11-819M15.2) + (0.15873 × FAM151B) + (0.142415 × EIF5A) + (0.151092 × SUMO2P17) + (0.081567 × FECH) + (0.046266 × MNX1-AS1)
Preferably, the disease to which the tumor corresponds includes breast cancer, colorectal adenocarcinoma, esophageal cancer, rectal adenocarcinoma, gastric adenocarcinoma, endometrial cancer, hepatocellular carcinoma, lung adenocarcinoma, lung squamous carcinoma, adrenocortical carcinoma, urinary bladder urothelial carcinoma, cervical squamous carcinoma and adenocarcinoma, diffuse large B-cell lymphoma, multiple-row glioblastoma, head and neck squamous cell carcinoma, renal chromophobe carcinoma, renal clear cell carcinoma, renal papillary cell carcinoma, acute myeloid leukemia, brain low-grade glioma, ovarian serous cystadenocarcinoma, uterine sarcoma, uveal melanoma, pancreatic cancer, pheochromocytoma and paraganglioma, prostate cancer, skin melanoma, sarcoma, thymus carcinoma.
Further, the cut-off values for each disease tumor were: breast cancer: 0.494, esophageal carcinoma 0.359, hepatocellular carcinoma 0.283, colon adenocarcinoma 0.364, lung adenocarcinoma 0.494, lung squamous carcinoma 0.926, rectal adenocarcinoma 0.431, gastric adenocarcinoma 0.297, endometrioma 0.053, adrenocortical carcinoma 0.154, urinary bladder urothelial carcinoma 0.157, cervical squamous carcinoma and adenocarcinoma 0.434, diffuse large B-cell lymphoma 0.484, glioblastoma multiforme 0.365, head and neck squamous cell carcinoma 0.424, renal chromophobe carcinoma 0.424, renal clear cell carcinoma 0.318, renal papillary cell carcinoma 0.333, acute myeloid leukemia 0.401, brain low-grade glioma 0.544, ovarian serous carcinoma 0.369, uterine sarcoma 0.243, uveal melanoma 0.151, pancreatic carcinoma 0.392, 594 chromium cell carcinoma and paraganglioma 0.522, prostate carcinoma 0.512, prostate carcinoma 0.392, and prostate carcinoma 0.540.
Further, when the GSVA score of the detection sample is higher than the cut-off value of the tumor, the detection sample is represented as the MSI-H state; when the GSVA score of the test sample is lower than the cut-off value of the tumor, the test sample is represented as MSI-L state.
Preferably, the test sample is a fresh tissue tumor sample.
Secondly, the invention provides the application of the reagent in preparing a kit for evaluating the disease prognosis survival.
Further, the disease includes breast cancer, colorectal adenocarcinoma, esophageal cancer, rectal adenocarcinoma, gastric adenocarcinoma, endometrial cancer, hepatocellular carcinoma, lung adenocarcinoma, lung squamous carcinoma, adrenocortical carcinoma, urinary bladder urothelial carcinoma, cervical squamous carcinoma and adenocarcinoma, diffuse large B-cell lymphoma, multiple-row glioblastoma, head and neck squamous cell carcinoma, renal chromophobe carcinoma, renal clear cell carcinoma, renal papillary cell carcinoma, acute myeloid leukemia, brain low-grade glioma, ovarian serous cystadenocarcinoma, uterine sarcoma, uveal melanoma, pancreatic cancer, pheochromocytoma and paraganglioma, prostate cancer, skin melanoma, sarcoma, thymus carcinoma.
Finally, the invention provides a method for evaluating the instability state of a tumor microsatellite, which comprises two processes of microsatellite detection site selection and microsatellite instability detection, wherein the microsatellite instability detection comprises the steps of firstly detecting the expression quantity of a sample gene/lncRNA (the gene/lncRNA is shown above), then comparing a numerical value obtained according to a GSVA scoring calculation formula (shown above) with a cutoff value (shown above) of the tumor (shown above), and when the obtained numerical value is higher than the cutoff value of the tumor, representing that a detection sample is in an MSI-H state; when the GSVA score of the test sample is lower than the cut-off value of the tumor, the test sample is represented as MSI-L state.
The invention has the advantages that:
the invention firstly comprehensively and systematically analyzes the whole genome expression data of 1400 samples, covers a plurality of tumors, and discovers PTMAP4, H3F3AP6, RPL22L1, RP11-267J23.4, MSH4, LYG1, RP11-372B4.3, TLE6, RP11-819M15.2, CIRP-AS 1, FAM151B, EIF5A, SUMO2P17, FECH and MNX1-AS1 which have significant differences in the tumors, and are suitable for judging MSI states. Subsequent studies further verify the application value of the gene set in MSI state calculation and prediction.
The invention firstly provides that the genome composition can be used for evaluating the instability state of the tumor microsatellite, and compared with the conventional method, the genome composition has the advantages of convenience in operation, quantitative analysis, high accuracy, and good sensitivity and specificity.
Drawings
FIG. 1 shows the efficacy of the MSI gene set in 6 tumors with unambiguous MSI status information.
FIG. 2 is the predicted potency of the MSI gene set in the remaining 23 tumors.
FIG. 3 is a comparison of specificity and sensitivity of different MSI prediction methods.
Detailed Description
The invention will be further illustrated with reference to specific embodiments. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Furthermore, it should be understood that various changes and modifications can be made by those skilled in the art after reading the disclosure of the present invention, and equivalents fall within the scope of the appended claims.
Example 1 screening of Gene set and Effect verification
Whole genome expression data analysis of six tumors
1 method
We first obtained mRNA sequencing data from the TCGA database for 1441 samples, including data from 6 tumor samples. We calculated the expression level of each gene, expressed as RSEM value. We then screened initially those genes that might be used for differential diagnosis of MSI-H and MSI-L based on the Mean ≧ 1000 criteria. Here, Mean represents the average expression level of a certain gene in tumor tissue. Then we screened the differential genes from small to large according to the adjusted P value <0.05, and screened 673 genes in total, wherein the expression in MSI-H is remarkably higher than 395 genes of MSI-L, and the expression in MSI-L is far higher than 278 genes of MSI-H. Then, the difference genes are selected to form a gene set, GSVA scores are respectively calculated, and then ROC curve verification is carried out.
2 results
TABLE 1 first 25 differential genes upregulated and downregulated in MSI-H
Because the GSVA algorithm requires consistency in the trend of gene changes, we compare the sets of up and down regulated genes separately. Based on a previous series of studies, we selected the gene set top10 to top25 to validate each tumor separately. Finally, by comparison, the best set of predicted genes is the set of factors consisting of MSI-H top15 upregulated genes: PTMAP4, H3F3AP6, RPL22L1, RP11-267J23.4, MSH4, LYG1, RP11-372B4.3, TLE6, RP11-819M15.2, CIRBP-AS1, FAM151B, EIF5A, SUMO2P17, FECH, MNX1-AS 1. It has a higher predictive potency among six tumors with a clear MSI status. The accuracy of all 5 other types except rectal adenocarcinoma (READ) was 88.5%, and the accuracy exceeded 90% (see fig. 1).
(II) Effect verification
Then, 6 tumors with definite MSI states are used as training sets and the rest 23 tumors without definite MSI state information are used as verification sets by using a machine learning random forest algorithm, so that the tumors are divided into two groups of MSI-H and MSI-L. The MSI-related gene sets were then used for external validation, and the ROC curves are shown in FIGS. 2A-2B. From the results, the accuracy of three tumors, except bladder urothelial cancer (BLCA, 71.3%), cervical squamous carcinoma and adenocarcinoma (CESC, 86.8%) and uveal melanoma (UVM, 66.7%), was less than 90%, and the predicted accuracy of MSI of the remaining tumors was greater than 90%, and was generally greater than 95%.
Comparative experiment (III)
In conclusion, the prediction efficiencies of the MSI gene set calculated by GSVA in different tumors are compared, and the MSI state can be accurately predicted by the gene set in most tumors. At the same time, we compared different methods to predict MSI status. Including the R language based PreMSIM function, predicted MSI status and the RNA-seq dataset based calculation of MSI scoring method at the NanoString nCounter platform proposed by Danaher et al. The former, although having a specificity almost similar to that of the method of the present invention, is not generally high in sensitivity. And the method is a qualitative method, and cannot quantitatively evaluate the MSI state well. The latter are generally not very sensitive and specific and are relatively complex. In conclusion, the method is a simple and convenient MSI prediction method capable of quantitatively calculating, and has certain advantages.
In the above experiment, the formula of the method related to the invention is as follows:
GSVA score-2.093101 + (0.012068 × PTMAP4) - (0.008544 × H3F3AP6) + (0.062508 × RPL22L1) + (0.073353 × RP11-267J23.4) + (0.111126 × MSH4) + (0.019458 × LYG1) + (0.119968 × RP11-372B4.3) + (0.099555 × TLE6) + (0.218096 × RP11-819M15.2) + (0.15873 × FAM151B) + (0.142415 × EIF5A) + (0.151092 × SUMO2P17) + (0.081567 × FECH) + (0.046266 × MNX1-AS 1);
the cut-off values for each disease tumor were: breast cancer: 0.494, esophageal carcinoma 0.359, hepatocellular carcinoma 0.283, colon adenocarcinoma 0.364, lung adenocarcinoma 0.494, lung squamous carcinoma 0.926, rectal adenocarcinoma 0.431, gastric adenocarcinoma 0.297, endometrioma 0.053, adrenocortical carcinoma 0.154, urinary bladder urothelial carcinoma 0.157, cervical squamous carcinoma and adenocarcinoma 0.434, diffuse large B-cell lymphoma 0.484, glioblastoma multiforme 0.365, head and neck squamous cell carcinoma 0.424, renal chromophobe carcinoma 0.424, renal clear cell carcinoma 0.318, renal papillary cell carcinoma 0.333, acute myeloid leukemia 0.401, brain low-grade glioma 0.544, ovarian serous carcinoma 0.369, uterine sarcoma 0.243, uveal melanoma 0.151, pancreatic carcinoma 0.392, 594 chromium cell carcinoma and paraganglioma 0.522, prostate carcinoma 0.512, prostate carcinoma 0.540;
when the GSVA score of the detection sample is higher than the cutoff value of the tumor, the detection sample is represented as the MSI-H state; when the GSVA score of the test sample is lower than the cut-off value of the tumor, the test sample is represented as MSI-L state.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and additions can be made without departing from the principle of the present invention, and these should also be considered as the protection scope of the present invention.
Claims (9)
1. The application of the detection reagent in preparing the kit for evaluating the instability state of the tumor microsatellite is characterized in that the detection reagent consists of reagents for detecting the expression quantity of the following genes/lncRNA: PTMAP4, H3F3AP6, RPL22L1, RP11-267J23.4, MSH4, LYG1, RP11-372B4.3, TLE6, RP11-819M15.2, CIRBP-AS1, FAM151B, EIF5A, SUMO2P17, FECH, MNX1-AS 1; the detection reagent is used as a kit to realize the evaluation of the only key component of the instability state of the tumor microsatellite.
2. The use of claim 1, further comprising instructions in the kit, the instructions describing the formula:
GSVA score-2.093101 + (0.012068 × PTMAP4) - (0.008544 × H3F3AP6) + (0.062508 × RPL22L1) + (0.073353 × RP11-267J23.4) + (0.111126 × MSH4) + (0.019458 × LYG1) + (0.119968 × RP11-372B4.3) + (0.099555 × TLE6) + (0.218096 × RP11-819M15.2) + (0.15873 × FAM151B) + (0.142415 × EIF5A) + (0.151092 × SUMO2P17) + (0.081567 × FECH) + (0.046266 × MNX1-AS 1).
3. The use of claim 1, wherein said neoplastic disease comprises breast cancer, colorectal adenocarcinoma, esophageal cancer, rectal adenocarcinoma, gastric adenocarcinoma, endometrial cancer, hepatocellular carcinoma, lung adenocarcinoma, lung squamous carcinoma, adrenocortical carcinoma, urothelial carcinoma of the bladder, cervical squamous carcinoma and adenocarcinoma, diffuse large B-cell lymphoma, multiple-row glioblastoma, head and neck squamous cell carcinoma, renal chromophobe carcinoma, renal clear cell carcinoma, renal papillary cell carcinoma, acute myeloid leukemia, brain low-grade glioma, ovarian serosa adenocarcinoma, uterine sarcoma, uveal melanoma, pancreatic cancer, pheochromocytoma and paraganglioma, prostate cancer, skin melanoma, sarcoma, thymus cancer.
4. The use according to claim 3, wherein the cut-off values for each disease tumor are: breast cancer: 0.494, esophageal carcinoma 0.359, hepatocellular carcinoma 0.283, colon adenocarcinoma 0.364, lung adenocarcinoma 0.494, lung squamous carcinoma 0.926, rectal adenocarcinoma 0.431, gastric adenocarcinoma 0.297, endometrioma 0.053, adrenocortical carcinoma 0.154, urinary bladder urothelial carcinoma 0.157, cervical squamous carcinoma and adenocarcinoma 0.434, diffuse large B-cell lymphoma 0.484, glioblastoma multiforme 0.365, head and neck squamous cell carcinoma 0.424, renal chromophobe carcinoma 0.424, renal clear cell carcinoma 0.318, renal papillary cell carcinoma 0.333, acute myeloid leukemia 0.401, brain low-grade glioma 0.544, ovarian serous carcinoma 0.369, uterine sarcoma 0.243, uveal melanoma 0.151, pancreatic carcinoma 0.392, 594 chromium cell carcinoma and paraganglioma 0.522, prostate carcinoma 0.512, prostate carcinoma 0.392, and prostate carcinoma 0.540.
5. The use of claim 4, wherein the MSI-H status is indicated in the test sample when the GSVA score of the test sample is higher than the cut-off value for the tumor; when the GSVA score of the test sample is lower than the cut-off value of the tumor, the test sample is represented as MSI-L state.
6. The use of claim 5, wherein the test sample is a fresh tissue tumor sample.
7. Use of the reagent of claim 1 for the preparation of a kit for assessing prognostic survival of a disease.
8. The use of claim 7, wherein the disease is selected from the group consisting of breast cancer, colorectal adenocarcinoma, esophageal cancer, rectal adenocarcinoma, gastric adenocarcinoma, endometrial cancer, hepatocellular carcinoma, lung adenocarcinoma, lung squamous carcinoma, adrenocortical carcinoma, urinary bladder epithelial carcinoma, cervical squamous carcinoma and adenocarcinoma, diffuse large B-cell lymphoma, multiple-row glioblastoma, head and neck squamous cell carcinoma, renal chromophobe carcinoma, renal clear cell carcinoma, renal papillary cell carcinoma, acute myeloid leukemia, brain low-grade glioma, ovarian serous cystadenocarcinoma, uterine sarcoma, uveal melanoma, pancreatic cancer, pheochromocytoma and paraganglioma, prostate cancer, skin melanoma, sarcoma, and thymus cancer.
9. A method for evaluating the instability state of a tumor microsatellite is characterized by comprising two processes of microsatellite detection site selection and microsatellite instability detection, wherein the microsatellite instability detection is to detect the expression quantity of a sample gene/lncRNA (ribonucleic acid/ribonucleic acid), then compare a numerical value obtained according to a GSVA (generalized likelihood of being accelerated) scoring calculation formula with a cutoff value of the tumor, and when the obtained numerical value is higher than the cutoff value of the tumor, represent that a detection sample is in an MSI-H state; when the GSVA score of the detection sample is lower than the cutoff value of the tumor, the detection sample is represented as the MSI-L state;
the gene/lncRNA consists of: PTMAP4, H3F3AP6, RPL22L1, RP11-267J23.4, MSH4, LYG1, RP11-372B4.3, TLE6, RP11-819M15.2, CIRBP-AS1, FAM151B, EIF5A, SUMO2P17, FECH, MNX1-AS 1;
the GSVA score is-2.093101 + (0.012068 xPTMAP 4) - (0.008544 xH 3F3AP6) + (0.062508 xRPL 22L1) + (0.073353 xRP 11-267J23.4) + (0.111126 xMSH 4) + (0.019458 xLYG 1) + (0.119968 xRP 11-372B4.3) + (0.099555 xTLE 6) + (0.218096 xRP 11-819M15.2) + (0.15873 xFAM 151B) + (0.142415 xEIF 5A) + (0.151092 xSUMO 2P17) + (0.081567 xFECH) + (0.046266 xMNX 1-AS1)
The tumor-corresponding diseases comprise breast cancer, colorectal adenocarcinoma, esophageal cancer, rectal adenocarcinoma, gastric adenocarcinoma, endometrial cancer, hepatocellular carcinoma, lung adenocarcinoma, lung squamous carcinoma, adrenocortical carcinoma, urinary bladder urothelial carcinoma, cervical squamous carcinoma and adenocarcinoma, diffuse large B-cell lymphoma, multiple-row glioblastoma, head and neck squamous cell carcinoma, renal chromophobe carcinoma, renal clear cell carcinoma, renal papillary cell carcinoma, acute myeloid leukemia, brain low-grade glioma, ovarian serous cystadenocarcinoma, uterine sarcoma, uveal melanoma, pancreatic cancer, pheochromocytoma and paraganglioma, prostate cancer, skin melanoma, sarcoma, thymus carcinoma;
the cut-off values for each disease tumor were: breast cancer: 0.494, esophageal carcinoma 0.359, hepatocellular carcinoma 0.283, colon adenocarcinoma 0.364, lung adenocarcinoma 0.494, lung squamous carcinoma 0.926, rectal adenocarcinoma 0.431, gastric adenocarcinoma 0.297, endometrioma 0.053, adrenocortical carcinoma 0.154, urinary bladder urothelial carcinoma 0.157, cervical squamous carcinoma and adenocarcinoma 0.434, diffuse large B-cell lymphoma 0.484, glioblastoma multiforme 0.365, head and neck squamous cell carcinoma 0.424, renal chromophobe carcinoma 0.424, renal clear cell carcinoma 0.318, renal papillary cell carcinoma 0.333, acute myeloid leukemia 0.401, brain low-grade glioma 0.544, ovarian serous carcinoma 0.369, uterine sarcoma 0.243, uveal melanoma 0.151, pancreatic carcinoma 0.392, 594 chromium cell carcinoma and paraganglioma 0.522, prostate carcinoma 0.512, prostate carcinoma 0.392, and prostate carcinoma 0.540.
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